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000916017 019__ $$a1124914955$$a1125114942$$a1125991405
000916017 020__ $$a9783030256142$$q(electronic book)
000916017 020__ $$a3030256146$$q(electronic book)
000916017 020__ $$z9783030256135
000916017 020__ $$z3030256138
000916017 0247_ $$a10.1007/978-3-030-25614-2$$2doi
000916017 0247_ $$a10.1007/978-3-030-25
000916017 035__ $$aSP(OCoLC)on1124957605
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000916017 08204 $$a621.382/2$$223
000916017 24500 $$aInpainting and denoising challenges /$$cSergio Escalera, Stephane Ayache, Jun Wan, Meysam Madadi, Umut Güçlü, Xavier Baró, editors.
000916017 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000916017 300__ $$a1 online resource (viii, 144 pages) :$$billustrations.
000916017 336__ $$atext$$btxt$$2rdacontent
000916017 337__ $$acomputer$$bc$$2rdamedia
000916017 338__ $$aonline resource$$bcr$$2rdacarrier
000916017 4901_ $$aThe Springer series on challenges in machine learning,$$x2520-131X
000916017 5050_ $$a1. A Brief Review of Image Denoising Algorithms and Beyond -- 2. ChaLearn Looking at People: Inpainting and Denoising Challenges -- 3. U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting -- 4. FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networks -- 5. Iterative Application of Autoencoders for Video Inpainting and Fingerprint Denoising -- 6. Video DeCaptioning using U-Net with Stacked Dilated Convolutional Layers -- 7. Joint Caption Detection and Inpainting using Generative Network -- 8. Generative Image Inpainting for Person Pose Generation -- 9. Person Inpainting with Generative Adversarial Networks -- 10. Road Layout Understanding by Generative Adversarial Inpainting -- 11. Photo-realistic and Robust Inpainting of Faces using Refinement GANs.
000916017 506__ $$aAccess limited to authorized users.
000916017 520__ $$aThe problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
000916017 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 25, 2019).
000916017 650_0 $$aSignal processing$$xDigital techniques.
000916017 650_0 $$aMachine learning.
000916017 7001_ $$aEscalera, Sergio,$$eeditor.
000916017 7001_ $$aAyache, Stephane,$$eeditor.
000916017 7001_ $$aWan, Jun,$$eeditor.
000916017 7001_ $$aMadadi, Meysam,$$eeditor.
000916017 7001_ $$aGüçlü, Umut,$$eeditor.
000916017 7001_ $$aBaró, Xavier,$$eeditor.
000916017 77608 $$iPrint version: $$z3030256138$$z9783030256135$$w(OCoLC)1104866920
000916017 830_0 $$aSpringer series on challenges in machine learning.
000916017 852__ $$bebk
000916017 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-25614-2$$zOnline Access$$91397441.1
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